Cytological and molecular diagnosis of Hürthle cell thyroid tumors: Analysis of three cases.

The cytological diagnosis of Hürthle cell (oncocytic) thyroid tumors by means of fine-needle aspiration biopsy represents a challenge, as Hürthle cell polymorphism and atypia alone are not indications of malignancy. In our recent work, an original diagnostic algorithm was proposed, which identified and typed malignant thyroid tumors by analyzing the molecular markers of cytological preparations. The aim of the present study was to assess the effectiveness of this algorithm at detecting Hürthle cell thyroid tumors in clinical samples used for cytological examination. Cytological and histological examinations of the biopsy material were performed for three patients with nodular neoplasms. Biopsy material of these patients was analyzed by quantitative PCR using preselected molecular markers [normalized concentrations of High-mobility group AT-hook 2 mRNA, three microRNAs (miRNAs or miRs; miR-146b, miR-221 and miR-375) and the mitochondrial (mtDNA)/nuclear DNA ratio]. The results revealed that the molecular test determined the malignancy of three cases of Hürthle cell tumor. This method may therefore be used to complement the cytological diagnosis of fine-needle aspiration biopsy. In all three cases, there was an increased content of mtDNA, indicating Hürthle cell malignancies. Furthermore, in the first case [Hürthle cell carcinoma (HCC)], increased miRNA-221 content was detected, which also indicated malignancy. In the second case (Hürthle cell papillary thyroid carcinoma), an increased level of miRNA-146b was present, which indicated papillary carcinoma. In the third case (Hürthle cell adenoma), no markers of malignancy were identified. The present study demonstrated that molecular testing together with cytological analysis can reduce the isk of error in the preoperative cytological diagnosis of unclear or ambivalent cases.

Selection and validation of miRNAs as normalizers for profiling expression of microRNAs isolated from thyroid fine needle aspiration smears.

Fine needle aspiration cytology (FNAC) is currently the method of choice for malignancy prediction in thyroid nodules. Nevertheless, in some cases the interpretation of FNAC results may be problematic due to limitations of the method. The expression level of some microRNAs changes with the development of thyroid tumors, and its quantitation can be used to refine the FNAC results. For this quantitation to be reliable, the obtained data must be adequately normalized. Currently, no reference genes are universally recognized for quantitative assessments of microRNAs in thyroid nodules. The aim of the present study was the selection and validation of such reference genes. Expression of 800 microRNAs in 5 paired samples of thyroid surgical material corresponding to different histotypes of tumors was analyzed using Nanostring technology and four of these (hsa-miR-151a-3p, -197-3p, -99a-5p and -214-3p) with the relatively low variation coefficient were selected. The possibility of use of the selected microRNAs and their combination as references was estimated by RT-qPCR on a sampling of cytological smears: benign (n=226), atypia of undetermined significance (n=9), suspicious for follicular neoplasm (n=61), suspicious for malignancy (n=19), medullary thyroid carcinoma (MTC) (n=32), papillary thyroid carcinoma (PTC) (n=54) and non-diagnostic material (ND) (n=34). In order to assess the expression stability of the references, geNorm algorithm was used. The maximum stability was observed for the normalization factor obtained by the combination of all 4 microRNAs. Further validation of the complex normalizer and individual selected microRNAs was performed using 5 different classification methods on 3 groups of FNAC smears from the analyzed batch: benign neoplasms, MTC and PTC. In all cases, the use of the complex classifier resulted in the reduced number of errors. On using the complex microRNA normalizer, the decision-tree method C4.5 makes it possible to distinguish between malignant and benign thyroid neoplasms in cytological smears with high overall accuracy (>91%).

miRNA profiling, detection of BRAF V600E mutation and RET-PTC1 translocation in patients from Novosibirsk oblast (Russia) with different types of thyroid tumors.

BACKGROUND: The postoperative typing of thyroid lesions, which is instrumental in adequate patient treatment, is currently based on histologic examination. However, it depends on pathologist's qualification and can be difficult in some cases. Numerous studies have shown that molecular markers such as microRNAs and somatic mutations may be useful to assist in these cases, but no consensus exists on the set of markers that is optimal for that purpose. The aim of the study was to discriminate between different thyroid neoplasms by RT-PCR, using a limited set of microRNAs selected from literature. METHODS: By RT-PCR we evaluated the relative levels of 15 microRNAs (miR-221, -222, -146b, -181b, -21, -187, -199b, -144, -192, -200a, -200b, -205, -141, -31, -375) and the presence of BRAF(V600E) mutation and RET-PTC1 translocation in surgically resected lesions from 208 patients from Novosibirsk oblast (Russia) with different types of thyroid neoplasms. Expression of each microRNA was normalized to adjacent non-tumor tissue. Three pieces of lesion tissue from each patient (39 goiters, 41 follicular adenomas, 16 follicular thyroid cancers, 108 papillary thyroid cancers, 4 medullary thyroid cancers) were analyzed independently to take into account method variation. RESULTS: The diagnostic classifier based on profiling of 13 microRNAs was proposed, with total estimated accuracy varying from 82.7 to 99% for different nodule types. Relative expression of six microRNAs (miR-146b, -21, -221, -222, 375, -199b) appeared significantly different in BRAF(V600E)-positive samples (all classified as papillary thyroid carcinomas) compared to BRAF(V600E)-negative papillary carcinoma samples. CONCLUSIONS: The results confirm practical feasibility of using molecular markers for typing of thyroid neoplasms and clarification of controversial cases.

Application of artificial neural network for classification of thyroid follicular tumors.

OBJECTIVE: To analyze smears of 197 thyroid follicular tumors (adenoma and carcinoma). STUDY DESIGN: Several types of artificial neural networks (ANN) of various designs were used for diagnosis of thyroid follicular tumors. The typical complex of cytologic features, some nuclear morphometric parameters (area, perimeter, shape factor) and density features of chromatin texture (mean value and SD of gray levels) were defined for each tumor. RESULTS: The ANN was trained by means of cytologic features characteristic for a thyroid follicular adenoma and a follicular carcinoma. At subsequent testing, the correct cytologic diagnosis was established in 93% (25 of 27) of cases. The morphometry increased the accuracy of diagnosis for follicular tumors in up to 97% (75 of 78) of cases. ANN correctly distinguished an adenoma or a carcinoma in 87% (73 of 84) of cases when using color microscopic images of tumors. CONCLUSION: The usage of ANN has raised sensitivity of cytologic diagnosis of follicular tumors to 90%, compared with a usual cytologic method (sensitivity of 56%). The automatic classification of thyroid follicular tumors by means of ANN is prospective.